Healthcare information technology system for predicting or preventing readmissions

ABSTRACT

Hospital readmissions may be prevented. Readmission is prevented by predicting the probability of a given patient to be readmitted. The probability alone may prevent readmission by educating the patient or medical professional. The probability may be predicted during a patient stay and used to generate a workflow action item to reduce the probability, to warn, to output appropriate instructions, and/or assist in avoiding readmission. The probability may be specific to a hospital, physician group, or other entity, allowing prevention to focus on past readmission causes for the given entity.

CROSS REFERENCE TO RELATED APPLICATIONS

This Application is a continuation application of U.S. patentapplication Ser. No. 14/225,549, filed Mar. 26, 2014, entitled“Healthcare Information Technology System for Predicting or PreventingReadmissions,” which is a continuation application of U.S. patentapplication Ser. No. 13/153,551 (U.S. Pat. No. 8,949,082), filed Jun. 6,2011, entitled “Healthcare Information Technology System for Predictingor Preventing Readmissions,” which claims priority from U.S. ProvisionalApplication No. 61/352,509, filed Jun. 8, 2010 and U.S. ProvisionalApplication No. 61/352,515, filed Jun. 8, 2010.

BACKGROUND

The present embodiments relate to predicting risk of hospitalreadmission and/or providing valuable information to potentially preventreadmission. Preventing readmission may reduce medical costs and benefitthe patient and hospital.

In the United States, about 20% of all Medicare beneficiaries arereadmitted, out of which 75% of the readmissions are potentiallypreventable. Examples of this include admission for angina followingdischarge for percutaneous transluminal coronary angioplasty (PTCA) oradmission for trauma following discharge for Acute Myocardial Infarction(AMI). The government and other private payers are focusing oncontrolling the costs associated with readmission. Preventablereadmission costs may amount to nearly $12 billion annually. The Centerfor Medicare and Medicaid Services (CMS) currently mandates publicreporting of readmission rates and payers may institute financialpenalties for poor performance and/or rewards for low readmissions.

With the recent stimulus and inevitable paradigm shift towardsaccountable care, organizations are focusing on cost reduction,standardized care, and quality improvement. There is a large, growingneed to help hospitals reduce preventable rate of readmissions toimprove quality of care and avoid financial and legal implications. Manyof these preventable readmissions are caused by discrepancies inpersonal health records that have not been updated with previous orcurrent admissions, medications (pre and post admission) not reconciledat the time of discharge, and no proper follow up with physicians ornurses.

SUMMARY

In various embodiments, systems, methods and computer readable media areprovided for predicting hospital readmission. Readmission is preventedby predicting the probability of a given patient to be readmitted. Theprobability alone may prevent readmission by educating the patient ormedical professional. The probability may be predicted at the time ofdischarge and used to generate a workflow action item to reduce theprobability, to warn, to output appropriate discharge instructions,and/or assist in avoiding readmission. The probability may be specificto a hospital, physician group, or other entity, allowing prevention tofocus on past readmission causes for the given entity.

In a first aspect, a method is provided for predicting hospitalreadmission. An indication of discharge of a patient from a medicalentity is received. Application of a predictor of readmission istriggered in response to the receiving of the indication. The predictorof the readmission is applied to an electronic medical record of thepatient in response to the triggering. The predictor is based onreadmission data of the medical entity. A probability of readmission ofthe patient is predicted based on applying the predictor to theelectronic medical record of the patient at discharge. An output isprovided as a function of the probability.

In a second aspect, a system is provided for predicting hospitalreadmission. At least one memory is operable to store data for aplurality of readmitted patients of a first medical entity. A firstprocessor is configured to: identify variables contributing toreadmission for the first medical entity based on the data for theplurality of the readmitted patients of the first medical entity, andincorporate the variables into a predictor of readmission for a futurepatient of the first medical entity.

In a third aspect, a non-transitory computer readable storage medium hasstored therein data representing instructions executable by a programmedprocessor for predicting hospital readmission. The storage mediumincludes instructions for predicting a probability of readmission of apatient, the predicting occurring at a time of discharge, comparing theprobability to a threshold, and generating an alert based on thecomparing, the generating occurring at the time of discharge.

Any one or more of the aspects described above may be used alone or incombination. These and other aspects, features and advantages willbecome apparent from the following detailed description of preferredembodiments, which is to be read in connection with the accompanyingdrawings. The present invention is defined by the following claims, andnothing in this section should be taken as a limitation on those claims.Further aspects and advantages of the invention are discussed below inconjunction with the preferred embodiments and may be later claimedindependently or in combination.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flow chart diagram of one embodiment of a method forpredicting readmission;

FIG. 2 is a block diagram of one embodiment of a computer processingsystem for mining patient data and/or using resulting mined data;

FIG. 3 shows an exemplary data mining framework for mining clinicalinformation; and

FIG. 4 shows an exemplary computerized patient record (CPR).

DESCRIPTION OF PREFERRED EMBODIMENTS

A majority of readmission cases may be prevented if the risk of thepatient to be readmitted is established as early as possible. The riskof readmission is calculated from the patient records (e.g., clinical,financial and demographic). For medical entity specific readmission, therisk is calculated by a classifier based on past patient data for themedical institution. For a current patient, the system identifieswhether the patient is at risk for readmission. The risk isautomatically calculated using a predictive model. The possible reasonsfor risk of a particular patient may be identified, and a plan formitigating the risk may be presented.

For generating the predictor from data of previous patients and forapplying the predictor for a current patient, patient data is obtainedfrom Electronic Medical Records (EMRs), such as patient informationdatabases, Radiology Information Systems (RIS), Pharmacological Records,or other form of medical data storage or representation. In an EMR orRIS, various data elements are normally associated to a patient orpatient visit, such as diagnosis codes, lab results, pharmacy,insurance, doctor notes, images, and genotypic information. Using themined data, a computer system predicts the risk of readmissions of apatient upon discharge and suggests optimal plans to mitigate this risk.

The tasks for predicting the risk of readmission of a patient areautomatically performed using this combination. Deviations anddiscrepancies may be identified, and mitigations to possibly prevent thereadmission may be output.

The risk of readmission may be specific to a given medical entity. Anymedical entity, such as a hospital, group of hospitals, group ofphysicians, region group (e.g., hospitals in a city, county, or state),office, insurance group, or other collection of medical professionalsassociated with patients, may contribute data to mitigation of risk ofreadmission. By using data associated with a specific medical entity,risk mitigation more focused on that entity rather than hospitals ingeneral may be provided.

For example, a hospital may have a greater risk of readmission forinfection than hospitals in a peer group. The data for patientspreviously admitted to the hospital is used to train a predictor. Amachine learns the factors at that hospital contributing to the risk ofreadmission. Manually input factors may be included as well. The factorsused, the relationship between factors, or relative weighting of thefactors is specific to the hospital. Upon discharge of a later patientof the hospital, the risk of readmission may be predicted. Given thehospital specific predictor, appropriate mitigation may be provided inresponse to the prediction. Alerts and associated workflow actions maybe output to reduce the risk of readmission for the patient.

FIG. 1 shows a method for preventing hospital readmission. The method isimplemented by or on a computer, server, processor, or other device. Themethod is provided in the order shown, but other orders may be provided.Additional, different or fewer acts may be provided. For example, acts402, 404, 406, 408, 412, 414, 416, or combinations thereof are notprovided. As another example, the mining for data of act 406 is notperformed as another source of information for prediction is provided.In another example, act 416 is not provided.

Continuous (real time) or periodic prediction of the risk of readmissionis performed. Throughout the hospital stay, the care provider may tunetheir care based on the most recent prediction. Given the rise inaccountable care where the care provider shares the financial risk,prediction before scheduling discharge allows alteration of the care ofthe patient in such a way that the risk of readmission is kept low asthe patient progresses on the floor. The risk may be predicted right atthe time the patient is admitted and/or other times. As the time passesand as more data (e.g., new labs results, new medications, newprocedures, existing history etc.) is gathered, the risk may be updatedcontinuously for the care provider to monitor.

In act 402, an indication of discharge of, admission of, or new data fora patient from a medical entity is received. The receipt is by acomputer or processor. For example, a nurse or administrator enters datafor the medical record of a patient indicating discharge. The entry maybe doctor instructions to discharge, may be that the patient is beingdischarged, may be scheduling of a discharge, or may be anotherdischarge related entry. As another example, a new data entry isprovided in the electronic medical record of the patient. In anotherexample, an assistant enters data showing admission or other key triggerevent (e.g., completion of surgery, assignment of the patient to anothercare group, or a change in patient status). In alternative embodiments,the indication is not received.

In act 404, application of a predictor of readmission is triggered. Thetrigger is in response to the receiving of the indication. An automatedworkflow is started in response to receiving the indication. The entryof discharge, admission, or other information causes a processor to runa prediction process.

The workflow determines whether there is an avoidable chance ofreadmission or a probability of readmission above a norm for a patient.This workflow occurs in response to discharge or scheduling discharge,admission, or other event of the patient. The triggered workflow beginsprior to, during or after patient discharge. In one embodiment, thetrigger occurs, at least in part, in real-time with patient dischargescheduling. While the patient is in the hospital and after determiningthat the patient is ready for discharge, the workflow for readmissionprediction is started. The workflow may be performed in real-time duringthe actual patient stay in other embodiments, such as at admission,periodically during a patient stay, or in response to other events ordata entry. The prediction may be performed after actual discharge.

In other embodiments, the prediction of risk for readmission istriggered based on an event other than discharge. For example, one ormore events identified as associated with readmission are used as atrigger. A clinical action, entry of medication or prescription,completion of surgery, or other entry or action triggers the applicationof the predictor for the patient. Where a given medical entity has aparticular concern for readmission, such as caused by failure toreconcile prescriptions, activity related to that concern may triggerapplication (e.g., triggering when an indication that a medication hasbeen prescribed). The triggering event may be different for differentmedical entities.

In act 406, the electronic medical record of the patient is mined. Topredict the risk of readmission, information is gathered. The classifierfor prediction has an input feature vector or group of variables usedfor prediction. The values for the variables for a particular patientare obtained by mining the electronic medical record for the patient.

The electronic medical record for the patient is a single database or acollection of databases. The record may include data at or fromdifferent medical entities, such as data from a database for a hospitaland data from a database for a primary care physician whether affiliatedor not with the hospital. Data for a patient may be mined from differenthospitals. Different databases at a same medical entity may be mined,such as mining a main patient data system, a separate radiology system(e.g., picture archiving and communication system), a separate pharmacysystem, a separate physician notes system, and/or a separate billingsystem. Different data sources for the same and/or different medicalentities are mined.

The different data sources have a same or different format. The miningis configured for the formats. For example, one, more, or all of thedata sources are of structured data. The data is stored as fields withdefined lengths, text limitations, or other characteristics. Each fieldis for a particular variable. The mining searches for and obtains thevalues from the desired fields. As another example, one, more, or all ofthe data sources are of unstructured data. Images, documents (e.g., freetext), or other collections of information without defined fields forvariables is unstructured. Physician notes may be grammatically correct,but the punctuation does not define values for specific variables. Themining may identify a value for one or more variables by searching forspecific criteria in the unstructured data.

Any now known or later developed mining may be used. For example, themining is of structured information. A specific data source or field issearched for a value for a specific variable. As another example, thevalues for variables are inferred. The values for different variablesare inferred by probabilistic combination of probabilities associatedwith different possible values from different sources. Each possiblevalue identified in one or more sources are assigned a probability basedon knowledge (statistically determined probabilities or professionallyassigned probabilities). The possible value to use as the actual valueis determined by probabilistic combination. The possible value with thehighest probability is selected. The selected values are inferred valuesfor the variables of the feature vector of the predictor of readmission.

U.S. Pat. No. 7,617,078, the disclosure of which is incorporated hereinby reference, shows a patient data mining method for combiningelectronic medical records for drawing conclusions. This system includesextraction, combination and inference components. The data to beextracted is present in the hospital electronic medical records in theform of clinical notes, procedural information, history and physicaldocuments, demographic information, medication records or otherinformation. The system combines local and global (possibly conflicting)evidences from medical records with medical knowledge and guidelines tomake inferences over time.

U.S. Published Application No. 2003/0120458, the disclosure of which isincorporated herein by reference, discloses mining unstructured andstructured information to extract structured clinical data. Missing,inconsistent or possibly incorrect information is dealt with throughassignment of probability or inference. These mining techniques are usedfor quality adherence (U.S. Published Application No. 2003/0125985),compliance (U.S. Published Application No. 2003/0125984), clinical trialqualification (U.S. Published Application No. 2003/0130871), and billing(U.S. Published Application No. 2004/0172297). The disclosures of thepublished applications referenced in the above paragraph areincorporated herein by reference. Other patent data mining for miningapproaches may be used, such as mining from only structured information,mining without assignment of probability, or mining without inferringfor inconsistent, missing or incorrect information. In alternativeembodiments, values are input by a user for applying the predictorwithout mining.

In act 408, a feature vector used for predicting the probability ispopulated. By mining, the values for variables are obtained. The featurevector is a list or group of variables used to predict the likelihood ofreadmission. The mining outputs values for the feature vector. Theoutput is in a structured format. The data from one or more datasources, such as an unstructured data source, is mined to determinevalues for specific variables. The values are in a structured formatvalues for defined fields are obtained.

The mining may provide all of the values, such as resolving anydiscrepancies based on probability. Any missing values may be replacedwith an average or predetermined value. The user may be requested toenter a missing value or resolve a choice between possible values for avariable. Alternatively, missing values are not replaced where thepredictor may operate with one or more of the values missing.

The feature vector is populated by assigning values to variables in aseparate data storage device or location. A table formatted for use bythe predictor is stored. Alternatively, the values are stored in thedata sources from which they are mined and pointers indicate thelocation for application of the predictor.

In act 410, the probability of readmission is predicted by applying thepredictor. The predictor is a classifier or model. In one embodiment,the predictor is a machine-trained classifier. Any machine training maybe used, such as training a statistical model (e.g., Bayesian network).The machine-trained classifier is any one or more classifiers. A singleclass or binary classifier, collection of different classifiers,cascaded classifiers, hierarchal classifier, multi-class classifier,model-based classifier, a classifier based on machine learning, orcombinations thereof may be used. Multi-class classifiers include CART,K-nearest neighbors, neural network (e.g., multi-layer perceptron),mixture models, or others. A probabilistic boosting tree may be used.Error-correcting output code (ECOC) may be used. In one embodiment, themachine-trained classifier is a probabilistic boosting tree classifier.The detector is a tree-based structure with which the posteriorprobabilities of readmission are calculated from given values ofvariables. The nodes in the tree are constructed by a nonlinearcombination of simple classifiers using boosting techniques. Theprobabilistic boosting tree (PBT) unifies classification, recognition,and clustering into one treatment. Alternatively, a programmed,knowledge based, or other classifier without machine learning is used.

For learning-based approaches, the classifier is taught to distinguishbased on features. For example, a probability model algorithmselectively combines features into a strong committee of weak learnersbased on values for available variables. As part of the machinelearning, some variables are selected and others are not selected. Thosevariables with the strongest or sufficient correlation or causalrelationship to readmission are selected and variables with little or nocorrelation or causal relationship are not selected. Features that arerelevant to readmission are extracted and learned in a machine algorithmbased on the ground truth of the training data, resulting in aprobabilistic model. Any size pool of features may be extracted, such astens, hundreds, or thousands of variables. The pool is determined by aprogrammer and/or may include features systematically determined by themachine. The training determines the most determinative features for agiven classification and discards lesser or non-determinative features.

The classifier is trained from a training data set using a computer. Toprepare the set of training samples, actual readmission is determinedfor each sample (e.g., for each patient represented in the training dataset, whether or after how long readmission occurred is determined). Anynumber of medical records for past patients is used. By using example ortraining data for tens, hundreds, or thousands of examples with knownreadmission status, a processor may determine the interrelationships ofdifferent variables to the outcome of readmission. The training data ismanually acquired or mining is used to determine the values of variablesin the training data. The training may be based on various criteria,such as readmission within a time period.

The training data is for the medical entity for which the predictor willbe applied. By using data for past patients of the same medical entity,the variables or feature vector most relevant to readmission for thatentity are determined. Different variables may be used by amachine-trained classifier for one medical entity than for anothermedical entity. Some of the training data may be from patients of otherentities, such as using half or more of the examples from other entitieswith similar readmission concerns, sizes, or patient populations. Thetraining data from the specific institution may skew or still result ina different machine-learnt classifier for the entity than using fewerexamples from the specific institution. In alternative embodiments, allof the training data is from other medical entities, or the predictor istrained in common for a plurality of different medical entities.

The classifier may be trained to predict based on different timeperiods, such as readmission within 30 days or after 1 year. Inalternative embodiments, the predictor is programmed, such as usingphysician knowledge or the results of studies.

The classifier is trained to predict readmission in general.Alternatively, separate classifiers are trained for different reasonsfor readmission, such as training a classifier for readmission fortrauma following discharge for acute myocardial infarction and anotherclassifier for readmission for angina following discharge forprecutaneous transluminal coronary angioplasty.

The learnt predictor is a matrix. The matrix provides weights fordifferent variables of the feature vectors. The values for the featurevector are weighted and combined based on the matrix. The predictor isapplied by inputting the feature vector to the matrix. Otherrepresentations than a matrix may be used.

For application, the predictor is applied to the electronic medicalrecord of a patient. In response to the triggering, the values of thevariables used by the learned classifier are obtained. The values areinput to the predictor as the feature vector. The predictor outputs aprobability of readmission of the patient based on the patient's currentelectronic medical record.

The probability of readmission is determined automatically. The user mayinput one or more values of variables into the electronic medicalrecord, but the prediction is performed without entry of values afterthe trigger and while applying the predictor. Alternatively, one or moreinputs are provided, such as resolving ambiguities in values or toselect an appropriate classifier (e.g., select a predictor ofreadmission for infection as opposed to readmission for trauma).

By applying the predictor to mined information for a patient, aprobability of readmission is predicted for that patient. Themachine-learnt or other classifier outputs a statistical probability ofreadmission based on the values of the variables for the patient. Wherethe prediction occurs in response to an event, such as triggering at therequest of a medical professional or administrator, the probability ispredicted from that time.

The classifier may indicate one or more values contributing to theprobability. For example, the failure to prescribe aspirin is identifiedas being the strongest link or contributor to a probability ofreadmission for a given patient being beyond a threshold. This variableand value are identified. The machine-learnt classifier may includestatistics or weights indicating the importance of different variablesto readmission and/or the normal. In combination with the values, someweighted values may more strongly determine an increased probability ofreadmission. Any deviation from norm may be highlighted. For example, avalue or weighted value of a variable a threshold amount different fromthe norm or mean is identified. The difference alone or in combinationwith the strength of contribution to the probability is considered inselecting one or more values as more significant. The more significantvalue or values may be identified.

The prediction is made during the patient stay. The prediction may berepeated at different times during the patient stay. The prediction maybe made at the time of discharge, such as the day of discharge. Theprediction may be updated, such as made before discharge and updatedafter discharge based on any data entered after the original prediction.

The probability of readmission is compared to one or more thresholds toestablish risk. The thresholds may be any probability based on nationalstandards, local standards, or other criteria. The medical entity mayset the thresholds to customize their definition of low, medium or highrisk patients. For example, the medical entity sets a threshold todistinguish a probability of readmission that is unusually high for thatmedical entity, for a similar class of medical entities, for entities ina region, for a rate important to reimbursement, or other grouping orconsideration.

The comparison may be used to identify a patient for which furtheraction may help reduce the probability of readmission. The comparisonmay be used to place the patient in a range. The output probabilityvalue may be used to classify the patient into different subgroups, suchas high, medium, or low risk of readmission.

In addition, appropriate quantification of severity (Low, Medium andHigh) may be used to reflect the stratification of risk. A differentclassifier or the same classifier weights the probability by the type ofreadmission. For more serious complications or reasons for readmission,a lesser probability may still be quantified as higher severity.

In alternative embodiments of creating and applying the predictor, theprediction of readmission is integrated as a variable to be mined. Theinference component determines the probability based on combination ofprobabilistic factoids or elements. The probability of readmission istreated as part of the patient state to be mined. Domain knowledgedetermines the variables used for combining to output the probability ofreadmission.

An output is provided in act 412. The output is a function of theprobability. The probability is used in a further workflow or output.For example, the probability causes a job or action item in a workflowin an effort to reduce the probability. As another example, theprobability with or without identification of the most significantlycontributing value or values and/or type of readmission predicted isused to recommend the type of follow-up, discharge instructions, orother clinical action.

This analysis may be performed in real time. If performed in real time,suggestions and/or corrections are output based on the probability. Thesuggestions and/or corrections may reduce the risk in a timely manner.Retrospective analysis may establish the top readmission reasons for thepatients at a particular institution medical entity and possibly suggestalternative workflows based on best clinical practices.

In one embodiment, an alert is generated based on the comparing of theprobability to the threshold or thresholds. The alert is generatedduring the patient stay, at the time of discharge (e.g., when a medicalprofessional is preparing discharge papers), or other times.

The alert is sent via text, email, voice mail, voice response, ornetwork notification. The alert indicates the level of risk ofreadmission, allowing mitigation when desired or appropriate. The alertis sent to the patient, family member, treating physician, nurse,primary care physician, and/or other medical professional. The alert maybe transmitted to a computer, cellular phone, tablet, bedside monitor ofthe patient, or other device. The recipient of the alert may examine whythe probability is beyond the threshold, determine changes in workflowto reduce the risk of readmission for other patients, and/or takeactions to reduce the risk for the patient for which the alert wasgenerated.

The alert indicates the patient and a risk of readmission. Otherinformation may be provided alternatively or additionally, such asidentification of one or more values and corresponding variablescorrelating with the severity or risk level.

In one embodiment, the alert is generated as a displayed warning whilepreventing entry of discharge or other information. The user isprevented from scheduling discharge or entering other data where theprobability of readmission and/or severity of predicated readmission aresufficiently high. In response to the user attempting to scheduledischarge or enter information associated with the patient, the alert isgenerated and the user is prevented from entering or saving theinformation. The prevention is temporary (e.g., seconds or minutes), mayremain until the probability has been reduced or require an over-ridefrom an authorized personnel (e.g. a case manager or an attendingphysician). The prevention may be for one type of data entry (e.g.,discharge scheduling) but allow another type (e.g., medicationreconciliation) to reduce the risk of readmission.

A user may be requested to enter additional information to help improvereadmissions rates in general, such as the user reconciling differentprescriptions, scheduling a follow-up, resolving discrepancies in theelectronic medical record, resolving a lack of adherence to a guideline,completing documentation in the electronic medical record, or arrangingfor a clinical action. The system may output a list of variables thatcan be considered to reduce the risk of readmission. At least onevariable having a value for the patient associated with a strong,stronger, or strongest link to the probability is output. For example, apatient has an unusually high measured blood characteristic, indicatinga possible infection. This high value may be the most significant reasonfor a probability of readmission above a threshold. Most significant orsignificant may be based on the weight for the variable and the value indetermining the probability or be based on a combination of factors(e.g., the relative strength or weight and the amount of deviance from athreshold). The strength of the link may be relative to links for othervalues of other variables to the risk of readmission. The reasons forthe risk of readmission are identified.

Recommendations may be made based on the identified variable, variables,or combination of variables. For example, based on the past and currentmedical records of a patient, it can be determined whether the personalhealth record of the patient has been updated or not with the currentadmission. Where the probability of readmission is based, at least inpart, on old information, a recommendation to document or update therecord is provided. Similarly, it can be highlighted whether themedications have been reconciled or not.

The recommendation is textual, such as providing instructions. Otherrecommendations may be visual. A visual representation of therelationship of the probability to the patient record may assist userunderstanding. The visual representation is output on a display orprinted. The visual representation of the relationship links elements orfactoids (variables) to the resulting risk of readmission. The valuesfor the variables from a specific patient record are inserted. Apictorial representation of the contribution of different variables,based on the values, to the risk may assist the user in generalunderstanding of how any conclusions are supported by inputs.

The visual representation shows the dependencies between the data andconclusions. The dependencies may be actual or imaginary. For example, amachine learning technique may be used. The relationship of a giveninput to the actual output may be unknown, but a statistical correlationmay be identified by machine learning. To assist in user understanding,a relationship may be graphically represented without actual dependency,such as probability or relative weighting, being known.

The visual representation may have any number of inputs, outputs, nodesor links. The types of data are shown. The relative contribution of aninput to a given output may be shown, such as colors, bold, or breadthof a link indicating a weight. The data source or sources used todetermine the values of the variables may be shown (e.g., billingrecord, prescription database or others).

The probability of readmission and/or variables associated with theprobability of readmission for a particular patient may be used todetermine a mitigation plan. The mitigation plan includes instructions,prescriptions, education materials, schedules, clinical actions, orother information that may reduce the risk of readmission. The nextrecommended clinical actions or reminders for the next recommendedclinical actions may be output so that health care personnel are betterable to follow the recommendations.

A library of mitigation plans is provided. Separate plans may beprovided for different reasons for possible readmission, differentvariables causing a higher risk of readmission, and/or differentcombinations of both. The plan or plans appropriate for a given patientare obtained and output.

The output may be based on a criteria set for the medical entity. Forexample, the medical entity may set the threshold for comparison to bemore or less inclusive of different levels of risk. As another example,the medical entity may select a combination of factors to trigger analert, such as probability level and types of variables contributing tothe probability level. If one variable causes the predictor to regularlyand inaccurately predict a risk higher than the threshold amount, thenpatients with higher probability based just or mostly on that variablemay not have an alert output or a different alert may be output.

The output may be discharge instructions for the patient and/or medicalprofessional (e.g., treating and/or primary care physician). Thedischarge instructions may include the mitigation plan. Alternatively oradditionally, the discharge instructions include the predictedprobability. Patients or physicians may be more likely to takecorrective or preventative actions where the probability of readmissionis known. The instruction may indicate the difference in probability ifa value is changed and by how much, showing benefit to change inbehavior or performance of clinical or medical action. Recommendationsmay be made to mitigate the risks. Semi-automated discharge instructionsbased on the longitudinal clinical record are created. In otherembodiments, the output is a mitigation plan to be performed during thepatient's stay.

An optimal follow-up strategy (e.g., phone call, in-home follow-up, orvisit to a doctor) may be provided in the instructions. The follow-upstrategy may be selected or determined based on the probability ofreadmission and/or the variables contributing to the probability ofreadmission being beyond the threshold. For example, an in-homefollow-up is scheduled for a probability further beyond the threshold(e.g., beyond another threshold in a stratification of risk), and aphone call is scheduled for a probability closer to the threshold (e.g.,for a lower risk). As another example, the severity of the type ofreadmission predicted is considered. The probability may be utilized tomanage the care and suggest possible and alternative care plans foroptimal patient outcomes.

In another embodiment, a job entry in a workflow is automaticallyscheduled as a function of the probability. A computerized workflowsystem includes action items to be performed by different individuals.The action items are communicated to the individual in a user interfacefor the workflow, by email, by text message, by placement in a calendar,or by other mechanism.

In act 414, a workflow job is generated for a case manager. The jobentry may be made to avoid readmission. The job entry may be to updatepatient data, arrange for clinical action, update a prescription,arrange for a prescription, review test results, arrange for testing,schedule a follow-up, review the probability, review patient data, orother action to reduce the probability of readmission. For example,where a follow-up is not scheduled during discharge and is notautomatically arranged, arranging for the follow-up may be placed as anaction item in an administrator's, assistant's, nurse's, or other casemanager's workflow. As another example, review of test results is placedin a physician's workflow so that appropriate action may be taken beforeor after discharge. A blood sample or other test may be performed atdischarge, but the results not available before discharge. This mayoccur, for example, where the predictor identifies a probability ofreadmission beyond the threshold due to missing information. The test isordered to provide the missing information. A workflow action isautomatically scheduled to examine the test results and take appropriateaction to avoid readmission. Similarly, a workflow action may bescheduled during the patient's stay to avoid a higher risk ofreadmission.

The workflow action item may be generated to review reasons forreadmission after readmission. Where a patient is readmitted, aretrospective analysis may be performed in an effort to identify whatcould or should have been done differently. A case manager, such as anadministrator of a hospital, may predict the probability of readmissionbased on the data at the time of the previous discharge or review thesaved probability. The discharge instructions, workflow action items, orother use of the probability may be examined to determine if otheraction was warranted. Future workflow action items, dischargeinstructions, physician education, or other actions may be performed toavoid similar reasons for readmission in other patients. A correlationstudy of readmitted patients may indicate common problems or trends.

The workflow is a separate application that queries the results of themining and/or prediction of probability of readmission. The workflowuses the results or is included as part of the predictor application.Any now known or later developed software or system providing a workflowengine may be configured to initiate a workflow based on data.

The workflow system may be configured to monitor adherence to the actionitems. Reminders may be automatically generated where an action item isdue or past due so that health care providers are better able to followthe recommendations.

Other predictors or statistical classifiers may be provided. One examplepredictor is for compliance by the patient with the discharge or otherinstructions. A level of risk (i.e., risk stratification) and/or reasonsfor risk are predicted. The ground truth for compliance may rely onpatient surveys or questionnaires. The predictor for whether a patientwill comply is trained from the training data. Different predictors maybe generated for different groups, such as by type of condition. Thevariables used for training may be the same or different than fortraining the predictor of readmission. The trained predictor ofcompliance may have a different or same feature vector as the predictorof readmission. Mining is performed to determine the values for trainingand/or the values for application.

At act 416, compliance is predicted. A predictor for compliance istriggered for application at the time of discharge or when otherinstructions are given to the patient, but may be performed at othertimes. The values of variables in the feature vector of the predictor ofcompliance are input to the predictor. The application of the predictorto the electronic medical record of the patient results in an outputprobability of compliance by the patient. The reasons for theprobability being beyond a threshold or thresholds may also be output,such as a lack of insurance or high medication cost contributing as astrong or stronger link to the probability being beyond the threshold.For example, a patient may be discharged to an unknown location (no homeor hospice listed in the discharge location variable). An unknownlocation may occur for homeless patients whom are less able to adhere toa care plan. The discharge location being unknown may be output so thata care provider may make subsequent care arrangements before dischargeor assign a case worker to assist with adherence.

The probability of compliance may be used to modify the discharge orother instructions and/or workflow action items. For example, the typeof follow-up may be more intensive or thorough where the probability ofcompliance is low. As another example, a workflow action may begenerated to identify alternative medicines where the cost of medicationis high. A consultation with a social worker may be arranged and/or thedischarge instructions based on lower cost alternatives may be providedwhere the patient does not have insurance.

FIG. 2 is a block diagram of an example computer processing system 100for implementing the embodiments described herein, such as preventinghospital or medical entity readmission. The systems, methods and/orcomputer readable media may be implemented in various forms of hardware,software, firmware, special purpose processors, or a combinationthereof. Some embodiments are implemented in software as a programtangibly embodied on a program storage device. By implementing with asystem or program, completely or semi-automated workflows, predictions,classifying, and/or data mining are provided to assist a person ormedical professional.

The system 100 is for generating a predictor, such as implementingmachine learning to train a statistical classifier. Alternatively oradditionally, the system 100 is for applying the predictor. The system100 may also implement associated workflows.

The system 100 is a computer, personal computer, server, PACsworkstation, imaging system, medical system, network processor, or othernow know or later developed processing system. The system 100 includesat least one processor (hereinafter processor) 102 operatively coupledto other components via a system bus 104. The program may be uploadedto, and executed by, a processor 102 comprising any suitablearchitecture. Likewise, processing strategies may includemultiprocessing, multitasking, parallel processing and the like. Theprocessor 102 is implemented on a computer platform having hardware suchas one or more central processing units (CPU), a random access memory(RAM) 106, a read-only memory (ROM) 108, and input/output (I/O)interface(s) 110. The computer platform also includes an operatingsystem and microinstruction code. The various processes and functionsdescribed herein may be either part of the microinstruction code or partof the program (or combination thereof) which is executed via theoperating system. Alternatively, the processor 102 is one or moreprocessors in a network and/or on an imaging system.

The processor 102 is configured to learn a classifier, such as creatinga predictor of readmission from training data, to mine the electronicmedical record of the patient or patients, and/or to apply amachine-learnt classifier to predict the probability of readmission.Training and application of a trained classifier are first discussedbelow. Example embodiments for mining follow.

For training, the processor 102 determines the relative or statisticalcontribution of different variables to the outcome, readmission. Aprogrammer may select variables to be considered. The programmer mayinfluence the training, such as assigning limitations on the number ofvariables and/or requiring inclusion of one or more variables to be usedas the input feature vector of the final classifier. By training, theclassifier identifies variables contributing to readmission. Where thetraining data is for patients from a given medical entity, the learningidentifies the variables most appropriate or determinative forreadmission based on discharge from that medical entity. The trainingincorporates the variables into a predictor of readmission for a futurepatient of the medical entity.

For application, the processor 102 applies the resulting(machine-learned) statistical model to the data for a patient. For eachpatient or for each patient in a category of patients (e.g., patientstreated for a specific condition or by a specific group within a medicalentity), the predictor is applied to the data for the patient. Thevalues for the identified and incorporated variables of themachine-learnt statistical model are input as a feature vector. A matrixof weights and combinations of weighted values calculates a probabilityof readmission.

The predictor of readmission may have any accuracy. For example, thereceiver operating characteristic (ROC) curve may show an area of about81% and a standard of deviation of about 1.7% where, at about 0% falsealarms, about 70% of the eventual readmissions are predicted. Otherperformance may be provided.

The processor 102 associates different workflows with different possiblepredictions of the predictor. The probability of readmission, theprobability of compliance, severity, and/or most determinative valuesmay be different for different patients. One or a combination of thesefactors is used to select an appropriate workflow or action. Differentpredictions or probabilities of readmission may result in different jobsto be performed and/or different instructions.

The processor 102 is operable to assign actions or to perform workflowactions. For example, the processor 102 initiates contact for follow-upby electronically notifying a patient in response to identifying aprobability of readmission. As another example, the processor 102requests documentation to resolve ambiguities in a medical record. Inanother example, the processor 102 generates a request for clinicalaction likely to decrease a probability of readmission. Clinical actionsmay include a test order, recommended action, request for patientinformation, other source of obtaining clinical information, orcombinations thereof. To decrease a probability of readmission, theprocessor 102 may generate a prescription form, clinical order (e.g.,test order), or other workflow action.

In a real-time usage, the processor 102 receives currently availablemedical information for a patient. Based on the currently availableinformation and mining the patient record, the processor 102 mayindicate how to mitigate risk of readmission. The actions may then beperformed during the treatment or before discharge.

The processor 102 implements the operations as part of the system 100 ora plurality of systems. The ROM 106, the RAM 108, the I/O interface 110,a network interface 112, and external storage 114 are operativelycoupled to the system bus 104 with the processor 102. Various peripheraldevices such as, for example, a display device, a disk storagedevice(e.g., a magnetic or optical disk storage device), a keyboard,printing device, and a mouse, may be operatively coupled to the systembus 104 by the I/O interface 110 or the network interface 112.

The computer system 100 may be a standalone system or be linked to anetwork via the network interface 112. The network interface 112 may bea hard-wired interface. However, in various exemplary embodiments, thenetwork interface 112 may include any device suitable to transmitinformation to and from another device, such as a universal asynchronousreceiver/transmitter (UART), a parallel digital interface, a softwareinterface or any combination of known or later developed software andhardware. The network interface may be linked to various types ofnetworks, including a local area network (LAN), a wide area network(WAN), an intranet, a virtual private network (VPN), and the Internet.

The instructions and/or patient record are stored in a non-transitorycomputer readable memory, such as the external storage 114. The same ordifferent computer readable media may be used for the instructions andthe patient record data. The external storage 114 may be implementedusing a database management system (DBMS) managed by the processor 102and residing on a memory such as a hard disk, RAM, or removable media.Alternatively, the storage 114 is internal to the processor 102 (e.g.cache). The external storage 114 may be implemented on one or moreadditional computer systems. For example, the external storage 114 mayinclude a data warehouse system residing on a separate computer system,a PACS system, or any other now known or later developed hospital,medical institution, medical office, testing facility, pharmacy or othermedical patient record storage system. The external storage 114, aninternal storage, other computer readable media, or combinations thereofstore data for at least one patient record for a patient. The patientrecord data may be distributed among multiple storage devices or in onelocation.

The patient data for training a machine learning classifier is stored.The training data includes data for patients that have been readmittedand data for patients that have not been readmitted after a selectedtime. The patients are for a same medical entity, group of medicalentities, region, or other collection.

Alternatively or additionally, the data for applying a machine-learntclassifier is stored. The data is for a patient being treated or readyfor discharge. The memory stores the electronic medical record of one ormore patients. Links to different data sources may be provided or thememory is made up of the different data sources. Alternatively, thememory stores extracted values for specific variables.

The instructions for implementing the processes, methods and/ortechniques discussed herein are provided on computer-readable storagemedia or memories, such as a cache, buffer, RAM, removable media, harddrive or other computer readable storage media. Computer readablestorage media include various types of volatile and nonvolatile storagemedia. The functions, acts or tasks illustrated in the figures ordescribed herein are executed in response to one or more sets ofinstructions stored in or on computer readable storage media. Thefunctions, acts or tasks are independent of the particular type ofinstructions set, storage media, processor or processing strategy andmay be performed by software, hardware, integrated circuits, firmware,micro code and the like, operating alone or in combination. In oneembodiment, the instructions are stored on a removable media device forreading by local or remote systems. In other embodiments, theinstructions are stored in a remote location for transfer through acomputer network or over telephone lines. In yet other embodiments, theinstructions are stored within a given computer, CPU, GPU or system.Because some of the constituent system components and method stepsdepicted in the accompanying figures are preferably implemented insoftware, the actual connections between the system components (or theprocess steps) may differ depending upon the manner in which the presentembodiments are programmed.

Health care providers may employ automated techniques for informationstorage and retrieval. The use of a computerized patient record (CPR)(e.g., an electronic medical record) to maintain patient information isone such example. As shown in FIG. 4 , an exemplary CPR 200 includesinformation collected over the course of a patient's treatment or use ofan institution. This information may include, for example, computedtomography (CT) images, X-ray images, laboratory test results, doctorprogress notes, details about medical procedures, prescription druginformation, radiological reports, other specialist reports, demographicinformation, family history, patient information, and billing(financial) information.

A CPR may include a plurality of data sources, each of which typicallyreflects a different aspect of a patient's care. Alternatively, the CPRis integrated into one data source. Structured data sources, such asfinancial, laboratory, and pharmacy databases, generally maintainpatient information in database tables. Information may also be storedin unstructured data sources, such as, for example, free text, images,and waveforms. Often, key clinical findings are only stored withinunstructured physician reports, annotations on images or otherunstructured data source.

Referring to FIG. 2 , the processor 102 executes the instructions storedin the computer readable media, such as the storage 114. Theinstructions are for mining patient records (e.g., the CPR), predictingreadmission, assigning workflow jobs, other functions, or combinationsthereof. For training and/or application of the predictor ofreadmission, values of variables are used. The values for particularpatients are mined from the CPR. The processor 102 mines the data toprovide values for the variables.

Any technique may be used for mining the patient record, such asstructured data based searching. In one embodiment, the methods, systemsand/or instructions disclosed in U.S. Published Application No.2003/0120458 are used, such as for mining from structured andunstructured patient records. FIG. 3 illustrates an exemplary datamining system implemented by the processor 102 for mining a patientrecord to create high-quality structured clinical information. Theprocessing components of the data mining system are software, firmware,microcode, hardware, combinations thereof, or other processor basedobjects. The data mining system includes a data miner 350 that minesinformation from a CPR 310 using domain-specific knowledge contained ina knowledge base 330. The data miner 350 includes extraction components352 for extracting information from the CPR 310, combination component354 for combining all available evidence in a principled fashion overtime, and inference component 356 for drawing inferences from thecombining. The mined information may be stored in a structured CPR 380.The architecture depicted in FIG. 4 supports plug-in modules wherein thesystem may be easily expanded for new data sources, diseases, andhospitals. New element extraction algorithms, element combiningalgorithms, and inference algorithms can be used to augment or replaceexisting algorithms.

The mining is performed as a function of domain knowledge. The domainknowledge provides an indication of reliability of a value based on thesource or context. For example, a note indicating the patient is asmoker may be accurate 90% of the time, so a 90% probability isassigned. A blood test showing nicotine may indicate that the patient isa smoker with 60% accuracy.

Detailed knowledge regarding the domain of interest, such as, forexample, a disease of interest, guides the process to identify relevantinformation. This domain knowledge base 330 can come in two forms. Itcan be encoded as an input to the system, or as programs that produceinformation that can be understood by the system. For example, a studydetermines factors contributing to readmission. These factors and theirrelationships may be used to mine for values. The study is used asdomain knowledge for the mining. Additionally or alternatively, thedomain knowledge base 330 may be learned from test data.

The domain-specific knowledge may also include disease-specific domainknowledge. For example, the disease-specific domain knowledge mayinclude various factors that influence risk of a disease, diseaseprogression information, complications information, outcomes, andvariables related to a disease, measurements related to a disease, andpolicies and guidelines established by medical bodies.

The information identified as relevant by the study, guidelines fortreatment, medical ontologies, or other sources provides an indicationof probability that a factor or item of information indicates or doesnot indicate a particular value of a variable. The relevance may beestimated in general, such as providing a relevance for any item ofinformation more likely to indicate a value as 75% or other probabilityabove 50%. The relevance may be more specific, such as assigning aprobability of the item of information indicating a particular diagnosisbased on clinical experience, tests, studies or machine learning. Basedon the domain-knowledge, the mining is performed as a function ofexisting knowledge, guidelines, or best practices regardingreadmissions. The domain knowledge indicates elements with a probabilitygreater than a threshold value of indicating the patient state (i.e.,collection of values). Other probabilities may be associated withcombinations of information.

Domain-specific knowledge for mining the data sources may includeinstitution-specific domain knowledge. For example, information aboutthe data available at a particular hospital, document structures at ahospital, policies of a hospital, guidelines of a hospital, and anyvariations of a hospital. The domain knowledge guides the mining, butmay guide without indicating a particular item of information from apatient record.

The extraction components 352 deal with gleaning small pieces ofinformation from each data source regarding a patient or plurality ofpatients. The pieces of information or elements are represented asprobabilistic assertions about the patient at a particular time.Alternatively, the elements are not associated with any probability. Theextraction components 352 take information from the CPR 310 to produceprobabilistic assertions (elements) about the patient that are relevantto an instant in time or period. This process is carried out with theguidance of the domain knowledge that is contained in the domainknowledge base 330. The domain knowledge for extraction is generallyspecific to each source, but may be generalized.

The data sources include structured and/or unstructured information.Structured information may be converted into standardized units, whereappropriate. Unstructured information may include ASCII text strings,image information in DICOM (Digital Imaging and Communication inMedicine) format, and text documents partitioned based on domainknowledge. Information that is likely to be incorrect or missing may benoted, so that action may be taken. For example, the mined informationmay include corrected information, including corrected ICD-9 diagnosiscodes.

Extraction from a database source may be carried out by querying a tablein the source, in which case, the domain knowledge encodes whatinformation is present in which fields in the database. On the otherhand, the extraction process may involve computing a complicatedfunction of the information contained in the database, in which case,the domain knowledge may be provided in the form of a program thatperforms this computation whose output may be fed to the rest of thesystem.

Extraction from images, waveforms, etc., may be carried out by imageprocessing or feature extraction programs that are provided to thesystem.

Extraction from a text source may be carried out by phrase spotting,which requires a list of rules that specify the phrases of interest andthe inferences that can be drawn there from. For example, if there is astatement in a doctor's note with the words “There is evidence ofmetastatic cancer in the liver,” then, in order to infer from thissentence that the patient has cancer, a rule is needed that directs thesystem to look for the phrase “metastatic cancer,” and, if it is found,to assert that the patient has cancer with a high degree of confidence(which, in the present embodiment, translates to generate an elementwith name “Cancer”, value “True” and confidence 0.9).

The combination component 354 combines all the elements that refer tothe same variable at the same time period to form one unifiedprobabilistic assertion regarding that variable. Combination includesthe process of producing a unified view of each variable at a givenpoint in time from potentially conflicting assertions from thesame/different sources. These unified probabilistic assertions arecalled factoids. The factoid is inferred from one or more elements.Where the different elements indicate different factoids or values for afactoid, the factoid with a sufficient (thresholded) or highestprobability from the probabilistic assertions is selected. The domainknowledge base may indicate the particular elements used. Alternatively,only elements with sufficient determinative probability are used. Theelements with a probability greater than a threshold of indicating apatient state (e.g., directly or indirectly as a factoid), are selected.In various embodiments, the combination is performed using domainknowledge regarding the statistics of the variables represented by theelements (“prior probabilities”).

The patient state is an individual model of the state of a patient. Thepatient state is a collection of variables that one may care aboutrelating to the patient, such as established by the domainknowledgebase. The information of interest may include a state sequence,i.e., the value of the patient state at different points in time duringthe patient's treatment.

The inference component 356 deals with the combination of thesefactoids, at the same point in time and/or at different points in time,to produce a coherent and concise picture of the progression of thepatient's state over time. This progression of the patient's state iscalled a state sequence. The patient state is inferred from the factoidsor elements. The patient state or states with a sufficient(thresholded), high probability or highest probability is selected as aninferred patient state or differential states.

Inference is the process of taking all the factoids and/or elements thatare available about a patient and producing a composite view of thepatient's progress through disease states, treatment protocols,laboratory tests, clinical action or combinations thereof. Essentially,a patient's current state can be influenced by a previous state and anynew composite observations. The risk for readmission may be consideredas a patient state so that the mining determines the risk without afurther application of a separate model.

The domain knowledge required for this process may be a statisticalmodel that describes the general pattern of the evolution of the diseaseof interest across the entire patient population and the relationshipsbetween the patient's disease and the variables that may be observed(lab test results, doctor's notes, or other information). A summary ofthe patient may be produced that is believed to be the most consistentwith the information contained in the factoids, and the domainknowledge.

For instance, if observations seem to state that a cancer patient isreceiving chemotherapy while he or she does not have cancerous growth,whereas the domain knowledge states that chemotherapy is given only whenthe patient has cancer, then the system may decide either: (1) thepatient does not have cancer and is not receiving chemotherapy (that is,the observation is probably incorrect), or (2) the patient has cancerand is receiving chemotherapy (the initial inference—that the patientdoes not have cancer—is incorrect); depending on which of thesepropositions is more likely given all the other information. Actually,both (1) and (2) may be concluded, but with different probabilities.

As another example, consider the situation where a statement such as“The patient has metastatic cancer” is found in a doctor's note, and itis concluded from that statement that <cancer=True (probability=0.9)>.(Note that this is equivalent to asserting that <cancer=True(probability=0.9), cancer=unknown (probability=0.1)>).

Now, further assume that there is a base probability of cancer<cancer=True (probability=0.35), cancer=False (probability=0.65)>(e.g.,35% of patients have cancer). Then, this assertion is combined with thebase probability of cancer to obtain, for example, the assertion<cancer=True (probability=0.93), cancer=False (probability=0.07)>.

Similarly, assume conflicting evidence indicated the following:

-   1. <cancer=True (probability=0.9), cancer=unknown probability=0.1)>-   2. <cancer=False (probability=0.7), cancer=unknown    (probability=0.3)>-   3. <cancer=True (probability=0.1), cancer=unknown (probability=0.9)>    and-   4. <cancer=False (probability=0.4), cancer=unknown    (probability=0.6)>.

In this case, we might combine these elements with the base probabilityof cancer <cancer=True (probability=0.35), cancer=False(probability=0.65)> to conclude, for example, that <cancer=True(prob=0.67), cancer=False (prob=0.33)>.

Numerous data sources may be assessed to gather the elements, and dealwith missing, incorrect, and/or inconsistent information. As an example,consider that, in determining whether a patient has diabetes, thefollowing information might be extracted:

-   (a) ICD-9 billing codes for secondary diagnoses associated with    diabetes;-   (b) drugs administered to the patient that are associated with the    treatment of diabetes (e.g., insulin);-   (c) patient's lab values that are diagnostic of diabetes (e.g., two    successive blood sugar readings over 250 mg/d);-   (d) doctor mentions that the patient is a diabetic in the H&P    (history & physical) or discharge note (free text); and-   (e) patient procedures (e.g., foot exam) associated with being a    diabetic.    As can be seen, there are multiple independent sources of    information, observations from which can support (with varying    degrees of certainty) that the patient is diabetic (or more    generally has some disease/condition). Not all of them may be    present, and in fact, in some cases, they may contradict each other.    Probabilistic observations can be derived, with varying degrees of    confidence. Then these observations (e.g., about the billing codes,    the drugs, the lab tests, etc.) may be probabilistically combined to    come up with a final probability of diabetes. Note that there may be    information in the patient record that contradicts diabetes. For    instance, the patient has some stressful episode (e.g., an    operation) and his blood sugar does not go up.

The above examples are presented for illustrative purposes only and arenot meant to be limiting. The actual manner in which elements arecombined depends on the particular domain under consideration as well asthe needs of the users of the system. Further, while the abovediscussion refers to a patient-centered approach, actual implementationsmay be extended to handle multiple patients simultaneously.Additionally, a learning process may be incorporated into the domainknowledge base 330 for any or all of the stages (i.e., extraction,combination, inference).

The system may be run at arbitrary intervals, periodic intervals, or inonline mode. When run at intervals, the data sources are mined when thesystem is run. In online mode, the data sources may be continuouslymined. The data miner may be run using the Internet. The createdstructured clinical information may also be accessed using the Internet.Additionally, the data miner may be run as a service. For example,several hospitals may participate in the service to have their patientinformation mined, and this information may be stored in a datawarehouse owned by the service provider. The service may be performed bya third party service provider (i.e., an entity not associated with thehospitals).

Once the structured CPR 380 is populated with patient information, itwill be in a form where it is conducive for answering questionsregarding individual patients, and about different cross-sections ofpatients. The values are available for use in predicting readmission.

The domain knowledgebase, extractions, combinations and/or inference maybe responsive or performed as a function of one or more variables. Forexample, the probabilistic assertions may ordinarily be associated withan average or mean value. However, some medical practitioners orinstitutions may desire that a particular element be more or lessindicative of a patient state. A different probability may be associatedwith an element. As another example, the group of elements included inthe domain knowledge base for a predictor of readmission may bedifferent for different medical entities. The threshold for sufficiencyof probability or other thresholds may be different for different peopleor situations.

Other variables may be use or institution specific. For example,different definitions of a primary care physician may be provided. Inone embodiment, a threshold for a number of visits may be used as adefinition of the primary care physician (e.g. visiting the same doctorfive times indicates the doctor is a primary care physician). In oneembodiment, a proximity to a patient's residence may be used.Additionally, combinations of factors may be used.

The user may select different settings. Different users in a sameinstitution or different institutions may use different settings. Thesame software or program operates differently based on receiving userinput. The input may be a selection of a specific setting or may beselection of a category associated with a group of settings.

The mining, such as the extraction, and/or the inferring, such as thecombination, are performed as a function of the selected threshold. Byusing a different upper limit of normal for the patient state, adifferent definition of information used in the domain knowledge orother threshold selection, the patient state or associated probabilitymay be different. Users with different goals or standards may use thesame program, but with the versatility to more likely fulfill the goalsor standards.

Various improvements described herein may be used together orseparately. Although illustrative embodiments of the present inventionhave been described herein with reference to the accompanying drawings,it is to be understood that the invention is not limited to thoseprecise embodiments, and that various other changes and modificationsmay be affected therein by one skilled in the art without departing fromthe scope or spirit of the invention.

What is claimed is:
 1. A method comprising: receiving an indication ofan event for a patient associated with a particular hospital;determining one or more hospital-specific variables that meet acorrelation with readmission at the particular hospital based ontraining data comprising hospital data identifying readmission forpatients at the particular hospital; generating, via one or moreprocessors, a predictive model of hospital-specific readmission for theparticular hospital that includes a plurality of machine-trainedclassifiers trained with the one or more hospital-specific variables,the plurality of machine-trained classifiers for predicting probabilityof readmission based on the one or more hospital-specific variables;automatically triggering, via the one or more processors, application ofthe predictive model of hospital-specific readmission to an electronichealth record that is specific to the patient in response to theindication of the event; in response to application of the predictivemodel of hospital-specific readmission, automatically predicting, viathe one or more processors, a probability of readmission of the patientfor the particular hospital based on the plurality of machine-trainedclassifiers and based on one or more values in the electronic healthrecord; determining, via the one or more processors, that theprobability of readmission meets a risk threshold; and presenting anotification based on the probability of readmission meeting the riskthreshold.
 2. The method of claim 1, wherein the event of the patient isan admission to the particular hospital, wherein the one or morehospital-specific variables are institution specific to the particularhospital, and wherein the training data comprises readmissioninformation for other patients who were readmitted to the hospital, thereadmission information comprising an amount of time prior toreadmission.
 3. The method of claim 1, wherein the event is a dischargefrom the particular hospital, an admission to the particular hospital,or a scheduled discharge from the particular hospital, and wherein thetraining data comprises readmission information for other patients whowere readmitted to the particular hospital and from at least one otherhospital.
 4. The method of claim 1, wherein at least onehospital-specific variable of the particular hospital differs from acorresponding hospital-specific variable of a second hospital, andfurther comprising: outputting a readmission indicator for the patientas a function of the probability of readmission, wherein outputting thereadmission indicator comprises generating one or more of a cell phonealert, a bedside monitor alert, and an alert associated with preventionof data entry.
 5. The method of claim 1, wherein the predictive model ofhospital-specific readmission differs from a predictive model ofhospital-specific readmission of a second hospital.
 6. The method ofclaim 1, further comprising: providing a list of one or more othervariables that are linked to a reduced probability of readmission of thepatient for the event; and based at least in part on the list, providinga recommended clinical action corresponding to a mitigation plan forreducing the probability of readmission of the patient for the event. 7.The method of claim 1, further comprising: assigning, using at least oneof the plurality of machine-trained classifiers, weights to the one ormore hospital-specific variables indicating a strength of a correlationof the one or more hospital-specific variables to the increasedprobability of readmission, wherein the one or more hospital-specificvariables are trained based on a plurality of hospital-specificpatients, and wherein automatically predicting the probability ofreadmission is based at least in part on the weights assigned to the oneor more hospital-specific variables.
 8. The method of claim 1, furthercomprising: mining information from structured and unstructured data inan electronic medical record, the mining performed as a function ofdomain knowledge comprising factors that influence a risk of a diseaseand progression information of the disease; and incorporating theinformation into the predictive model, wherein automatically predictingthe probability of readmission is based at least in part onincorporating the information.
 9. The method of claim 8, wherein themining is configured for different formats of data sources withinelectronic medical records of the particular hospital and at least oneother hospital, and wherein the information comprises a value for theone or more hospital-specific variables and an inferred value for theone or more hospital-specific variables.
 10. The method of claim 9,wherein the domain knowledge provides an indication of reliability ofthe value and the inferred value based on a corresponding data source ora context.
 11. A system comprising: at least one memory; and one or moreprocessors configured to: receive an indication of an event for apatient associated with a particular hospital; determine one or morehospital-specific variables that meet a correlation with readmission atthe particular hospital based on from training data comprising hospitaldata identifying readmission for patients at the particular hospital;machine-train a plurality of classifiers using the one or morehospital-specific variables; generate a predictive model ofhospital-specific readmission for the particular hospital that includesthe plurality of classifiers, the plurality of machine-trainedclassifiers for predicting probability of readmission based on the oneor more hospital-specific variables; automatically trigger applicationof the predictive model of hospital-specific readmission to anelectronic health record that is specific to the patient in response tothe indication; in response to application of the predictive model ofhospital-specific readmission, automatically predict a probability ofreadmission of the patient for the particular hospital based on theplurality of machine-trained classifiers and based on one or more valuesin the electronic health record; determine that the probability ofreadmission meets a risk threshold; and present a notification based onthe probability of readmission meeting the risk threshold.
 12. Thesystem of claim 11, wherein the training data comprises readmission datafor patients readmitted to the particular hospital within a particulartime period, and wherein at least one hospital-specific variable of theparticular hospital differs from a corresponding hospital-specificvariable of a second hospital.
 13. The system of claim 11, wherein theone or more hospital-specific variables used to train the plurality ofclassifiers are from data of patients readmitted to the particularhospital for admission problems, and wherein the one or more processersare configured to: machine-train each of the plurality of classifiersusing the one or more hospital-specific variables from the training dataof patients who were readmitted to the hospital for a correspondingadmission problem following a discharge; wherein the probability ofreadmission of the patient is automatically predicted for thecorresponding admission problem.
 14. The system of claim 11, wherein theone or more processors are configured to: receive a selection to alter athreshold associated with at least one of the plurality of classifiersin the predictive model, and wherein the probability of readmission ofthe patient is automatically predicted based at least in part on thealtered threshold.
 15. The system of claim 11, wherein the one or moreprocessors are configured to: update the training data in response toprevious patients readmitted to the hospital after receiving one or moreof a new or different prescription, a follow-up, or completingdocumentation in an electronic medical record, wherein the probabilityof readmission of the patient is automatically predicted based at leastin part on the updated training data.
 16. The system of claim 11,wherein the one or more processors are configured to: identify that theprobability of readmission is based on missing information.
 17. Thesystem of claim 11, wherein the one or more processors are configuredto: mine domain-specific knowledge of the particular hospital includingone or more document structures, policies, or guidelines to determinethe one or more hospital-specific variables.
 18. One or morenon-transitory computer storage media having computer-executableinstructions embodied thereon, that when executed by at least oneprocessor, cause a computer system to perform a method, the methodcomprising: via the at least one processor: receiving an indication ofan event for a patient associated with a particular hospital;determining one or more hospital-specific variables that meet acorrelation with readmission at the particular hospital based onelectronic medical records of patients having events corresponding tothe event for the patient, wherein the one or more hospital-specificvariables are linked to a probability of readmission for the particularhospital; generating a predictive model of hospital-specific readmissionfor the particular hospital that includes a plurality of machine-trainedclassifiers that are trained with the one or more hospital-specificvariables; automatically triggering application of the predictive modelof hospital-specific readmission to an electronic health record that isspecific to the patient in response to the indication; in response tothe application of the predictive model of hospital-specificreadmission, automatically predicting a probability of readmission ofthe patient for the particular hospital based on the plurality ofmachine-trained classifiers and based on one or more values in theelectronic health record; determining that the probability ofreadmission meets a risk threshold; and presenting a notification basedon the probability of readmission meeting the risk threshold.
 19. Themedia of claim 18, wherein the electronic medical records of thepatients are from the particular hospital.
 20. The media of claim 18,the method caused to be performed by the computer system furthercomprising: extracting elements of the one or more variables from theelectronic medical records of the patients, the elements correspondingto a time between the event and readmission; combining the elements toform unified probabilistic assertion regarding one of the one or morehospital-specific variables; inferring a progression of a state of thepatient over time from the unified probabilistic assertion; andpopulating a predictor with the progression of the state of the patientover time.